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Scrivener CL, Reader AT. Variability of EEG electrode positions and their underlying brain regions: visualizing gel artifacts from a simultaneous EEG-fMRI dataset. Brain Behav 2022; 12:e2476. [PMID: 35040596 PMCID: PMC8865144 DOI: 10.1002/brb3.2476] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 12/09/2021] [Accepted: 12/14/2021] [Indexed: 11/11/2022] Open
Abstract
INTRODUCTION We investigated the between-subject variability of EEG (electroencephalography) electrode placement from a simultaneously recorded EEG-fMRI (functional magnetic resonance imaging) dataset. METHODS Neuro-navigation software was used to localize electrode positions, made possible by the gel artifacts present in the structural magnetic resonance images. To assess variation in the brain regions directly underneath electrodes we used MNI coordinates, their associated Brodmann areas, and labels from the Harvard-Oxford Cortical Atlas. We outline this relatively simple pipeline with accompanying analysis code. RESULTS In a sample of 20 participants, the mean standard deviation of electrode placement was 3.94 mm in x, 5.55 mm in y, and 7.17 mm in z, with the largest variation in parietal and occipital electrodes. In addition, the brain regions covered by electrode pairs were not always consistent; for example, the mean location of electrode PO7 was mapped to BA18 (secondary visual cortex), whereas PO8 was closer to BA19 (visual association cortex). Further, electrode C1 was mapped to BA4 (primary motor cortex), whereas C2 was closer to BA6 (premotor cortex). CONCLUSIONS Overall, the results emphasize the variation in electrode positioning that can be found even in a fixed cap. This may be particularly important to consider when using EEG positioning systems to inform non-invasive neurostimulation.
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Affiliation(s)
- Catriona L Scrivener
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.,School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
| | - Arran T Reader
- Department of Psychology, Faculty of Natural Sciences, University of Stirling, Stirling, UK
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Pinte C, Fleury M, Maurel P. Deep Learning-Based Localization of EEG Electrodes Within MRI Acquisitions. Front Neurol 2021; 12:644278. [PMID: 34305777 PMCID: PMC8296904 DOI: 10.3389/fneur.2021.644278] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Accepted: 06/07/2021] [Indexed: 02/02/2023] Open
Abstract
The simultaneous acquisition of electroencephalographic (EEG) signals and functional magnetic resonance images (fMRI) aims to measure brain activity with good spatial and temporal resolution. This bimodal neuroimaging can bring complementary and very relevant information in many cases and in particular for epilepsy. Indeed, it has been shown that it can facilitate the localization of epileptic networks. Regarding the EEG, source localization requires the resolution of a complex inverse problem that depends on several parameters, one of the most important of which is the position of the EEG electrodes on the scalp. These positions are often roughly estimated using fiducial points. In simultaneous EEG-fMRI acquisitions, specific MRI sequences can provide valuable spatial information. In this work, we propose a new fully automatic method based on neural networks to segment an ultra-short echo-time MR volume in order to retrieve the coordinates and labels of the EEG electrodes. It consists of two steps: a segmentation of the images by a neural network, followed by the registration of an EEG template on the obtained detections. We trained the neural network using 37 MR volumes and then we tested our method on 23 new volumes. The results show an average detection accuracy of 99.7% with an average position error of 2.24 mm, as well as 100% accuracy in the labeling.
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Affiliation(s)
- Caroline Pinte
- Univ Rennes, Inria, CNRS, Inserm, Empenn ERL U1228, Rennes, France
| | - Mathis Fleury
- Univ Rennes, Inria, CNRS, Inserm, Empenn ERL U1228, Rennes, France
| | - Pierre Maurel
- Univ Rennes, Inria, CNRS, Inserm, Empenn ERL U1228, Rennes, France
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Scrivener CL. When Is Simultaneous Recording Necessary? A Guide for Researchers Considering Combined EEG-fMRI. Front Neurosci 2021; 15:636424. [PMID: 34267620 PMCID: PMC8276697 DOI: 10.3389/fnins.2021.636424] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 06/01/2021] [Indexed: 11/19/2022] Open
Abstract
Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide non-invasive measures of brain activity at varying spatial and temporal scales, offering different views on brain function for both clinical and experimental applications. Simultaneous recording of these measures attempts to maximize the respective strengths of each method, while compensating for their weaknesses. However, combined recording is not necessary to address all research questions of interest, and experiments may have greater statistical power to detect effects by maximizing the signal-to-noise ratio in separate recording sessions. While several existing papers discuss the reasons for or against combined recording, this article aims to synthesize these arguments into a flow chart of questions that researchers can consider when deciding whether to record EEG and fMRI separately or simultaneously. Given the potential advantages of simultaneous EEG-fMRI, the aim is to provide an initial overview of the most important concepts and to direct readers to relevant literature that will aid them in this decision.
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Affiliation(s)
- Catriona L. Scrivener
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
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Bhutada AS, Sepúlveda P, Torres R, Ossandón T, Ruiz S, Sitaram R. Semi-Automated and Direct Localization and Labeling of EEG Electrodes Using MR Structural Images for Simultaneous fMRI-EEG. Front Neurosci 2021; 14:558981. [PMID: 33414699 PMCID: PMC7783406 DOI: 10.3389/fnins.2020.558981] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 10/08/2020] [Indexed: 11/13/2022] Open
Abstract
Electroencephalography (EEG) source reconstruction estimates spatial information from the brain’s electrical activity acquired using EEG. This method requires accurate identification of the EEG electrodes in a three-dimensional (3D) space and involves spatial localization and labeling of EEG electrodes. Here, we propose a new approach to tackle this two-step problem based on the simultaneous acquisition of EEG and magnetic resonance imaging (MRI). For the step of spatial localization of electrodes, we extract the electrode coordinates from the curvature of the protrusions formed in the high-resolution T1-weighted brain scans. In the next step, we assign labels to each electrode based on the distinguishing feature of the electrode’s distance profile in relation to other electrodes. We then compare the subject’s electrode data with template-based models of prelabeled distance profiles of correctly labeled subjects. Based on this approach, we could localize EEG electrodes in 26 head models with over 90% accuracy in the 3D localization of electrodes. Next, we performed electrode labeling of the subjects’ data with progressive improvements in accuracy: with ∼58% accuracy based on a single EEG-template, with ∼71% accuracy based on 3 EEG-templates, and with ∼76% accuracy using 5 EEG-templates. The proposed semi-automated method provides a simple alternative for the rapid localization and labeling of electrodes without the requirement of any additional equipment than what is already used in an EEG-fMRI setup.
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Affiliation(s)
- Abhishek S Bhutada
- Department of Molecular and Cellular Biology, University of California, Berkeley, Berkeley, CA, United States
| | - Pradyumna Sepúlveda
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Rafael Torres
- Department of Psychiatry, Faculty of Medicine, Interdisciplinary Center for Neuroscience, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Tomás Ossandón
- Department of Psychiatry, Faculty of Medicine, Interdisciplinary Center for Neuroscience, Pontificia Universidad Católica de Chile, Santiago, Chile.,Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Sergio Ruiz
- Department of Psychiatry, Faculty of Medicine, Interdisciplinary Center for Neuroscience, Pontificia Universidad Católica de Chile, Santiago, Chile.,Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Ranganatha Sitaram
- Department of Psychiatry, Faculty of Medicine, Interdisciplinary Center for Neuroscience, Pontificia Universidad Católica de Chile, Santiago, Chile.,Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile.,Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
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A Rapid Form of Offline Consolidation in Skill Learning. Curr Biol 2019; 29:1346-1351.e4. [PMID: 30930043 DOI: 10.1016/j.cub.2019.02.049] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2019] [Revised: 02/01/2019] [Accepted: 02/21/2019] [Indexed: 12/12/2022]
Abstract
The brain strengthens memories through consolidation, defined as resistance to interference (stabilization) or performance improvements between the end of a practice session and the beginning of the next (offline gains) [1]. Typically, consolidation has been measured hours or days after the completion of training [2], but the same concept may apply to periods of rest that occur interspersed in a series of practice bouts within the same session. Here, we took an unprecedented close look at the within-seconds time course of early human procedural learning over alternating short periods of practice and rest that constitute a typical online training session. We found that performance did not markedly change over short periods of practice. On the other hand, performance improvements in between practice periods, when subjects were at rest, were significant and accounted for early procedural learning. These offline improvements were more prominent in early training trials when the learning curve was steep and no performance decrements during preceding practice periods were present. At the neural level, simultaneous magnetoencephalographic recordings showed an anatomically defined signature of this phenomenon. Beta-band brain oscillatory activity in a predominantly contralateral frontoparietal network predicted rest-period performance improvements. Consistent with its role in sensorimotor engagement [3], modulation of beta activity may reflect replay of task processes during rest periods. We report a rapid form of offline consolidation that substantially contributes to early skill learning and may extend the concept of consolidation to a time scale in the order of seconds, rather than the hours or days traditionally accepted.
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Butler R, Gilbert G, Descoteaux M, Bernier PM, Whittingstall K. Application of polymer sensitive MRI sequence to localization of EEG electrodes. J Neurosci Methods 2016; 278:36-45. [PMID: 28017737 DOI: 10.1016/j.jneumeth.2016.12.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2016] [Revised: 11/05/2016] [Accepted: 12/20/2016] [Indexed: 11/17/2022]
Abstract
BACKGROUND The growing popularity of simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) opens up the possibility of imaging EEG electrodes while the subject is in the scanner. Such information could be useful for improving the fusion of EEG-fMRI datasets. NEW METHOD Here, we report for the first time how an ultra-short echo time (UTE) MR sequence can image the materials of an MR-compatible EEG cap, finding that electrodes and some parts of the wiring are visible in a high resolution UTE. Using these images, we developed a segmentation procedure to obtain electrode coordinates based on voxel intensity from the raw UTE, using hand labeled coordinates as the starting point. RESULTS We were able to visualize and segment 95% of EEG electrodes using a short (3.5min) UTE sequence. We provide scripts and template images so this approach can now be easily implemented to obtain precise, subject-specific EEG electrode positions while adding minimal acquisition time to the simultaneous EEG-fMRI protocol. COMPARISON WITH EXISTING METHOD(S) T1 gel artifacts are not robust enough to localize all electrodes across subjects, the polymers composing Brainvision cap electrodes are not visible on a T1, and adding T1 visible materials to the EEG cap is not always possible. We therefore consider our method superior to existing methods for obtaining electrode positions in the scanner, as it is hardware free and should work on a wide range of materials (caps). CONCLUSIONS EEG electrode positions are obtained with high precision and no additional hardware.
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Affiliation(s)
- Russell Butler
- Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, Québec, Canada; Centre d'imagerie moléculaire de Sherbrooke (CIMS), Centre de Recherche CHUS, Canada.
| | - Guillaume Gilbert
- MR Clinical Science, Philips Healthcare Canada, 281 H illmount Road, Markham, Ontario, L6C 2S3, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science department, Science Faculty, Université de Sherbrooke, Canada; Centre d'imagerie moléculaire de Sherbrooke (CIMS), Centre de Recherche CHUS, Canada
| | | | - Kevin Whittingstall
- Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, Québec, Canada; Department of Diagnostic Radiology, Faculty of Medicine and Health Science, Université de Sherbrooke, 12e Avenue Nord, Sherbrooke, QC, J1H 5N4, Canada; Centre d'imagerie moléculaire de Sherbrooke (CIMS), Centre de Recherche CHUS, Canada
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Marino M, Liu Q, Brem S, Wenderoth N, Mantini D. Automated detection and labeling of high-density EEG electrodes from structural MR images. J Neural Eng 2016; 13:056003. [DOI: 10.1088/1741-2560/13/5/056003] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Kowalczyk L, Bajera A, Goszczynska H, Zalewska E, Krolicki L. Integration of EEG and SPECT data acquired from simultaneous examinations. Biocybern Biomed Eng 2013. [DOI: 10.1016/j.bbe.2013.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Feasibility of clinical magnetoencephalography (MEG) functional mapping in the presence of dental artefacts. Clin Neurophysiol 2012; 124:107-13. [PMID: 22832101 DOI: 10.1016/j.clinph.2012.06.013] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2011] [Revised: 06/14/2012] [Accepted: 06/18/2012] [Indexed: 11/20/2022]
Abstract
OBJECTIVE To evaluate the viability of MEG source reconstruction in the presence of large interference due to orthodontic material. METHODS We recorded the magnetic fields following a simple hand movement and following electrical stimulation of the median nerve (somatosensory evoked field -SEF). These two tasks were performed twice, once with and once without artificial dental artefacts. Temporal Signal Space Separation (tSSS) was applied to spatially filter the data and source reconstruction was performed according to standard procedures for pre-surgical mapping of eloquent cortex, applying dipole fitting to the SEF data and beamforming to the hand movement data. RESULTS Comparing the data with braces to the data without braces, the observed distances between the activations following hand movement in the two conditions were on average 6.4 and 4.5 mm for the left and right hand, respectively, whereas the dipole localisation errors for the SEF were 4.1 and 5.4 mm, respectively. Without tSSS it was generally not possible to obtain reliable dipole fit or beamforming results when wearing braces. CONCLUSION We confirm that tSSS is a required and effective pre-processing step for data recorded with the Elekta-MEG system. Moreover, we have shown that even the presence of large interference from orthodontic material does not significantly alter the results from dipole localisation or beamformer analysis, provided the data are spatially filtered by tSSS. SIGNIFICANCE State-of-the-art signal processing techniques enable the use of MEG for pre-surgical evaluation in a much larger clinical population than previously thought possible.
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de Munck J, van Houdt P, Verdaasdonk R, Ossenblok P. Artifacts in EEG/fMRI: Challenges and opportunities. Neurophysiol Clin 2012. [DOI: 10.1016/j.neucli.2011.11.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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